Interference detection for hierarchical license deployment based on machine learning

Abstract

Examples of interference detection for hierarchical license deployment based on machine learning are described. A network entity in a generic licensed access (GAA) deployment checks a local spectrum access database for GAA users to determine that portions of shared usage spectrum are not occupied by known local users in a geographic area. The network entity receives samples of a wireless signal comprising a desired signal at least on the portion of the shared use spectrum. The network entity determines whether the wireless signal includes a plurality of independent signals. The network entity identifies an interfering signal in response to determining that the wireless signal includes the plurality of independent signals.

Description

Background Art

[0001] A radio access network (RAN) can provide wireless access to a network to multiple user devices. User devices can communicate wirelessly with a base station, which forwards the communication to a core network. Typically, a base station in a RAN is implemented by dedicated processing hardware (e.g., an embedded system) located near a radio unit including an antenna. A base station can perform lower layer processing including physical (PHY) layer and media access control (MAC) layer processing for one or more cells. There may be associated costs in deploying dedicated processing hardware for each base station in a RAN, particularly for a RAN that includes small cells with relatively small coverage areas. Additionally, the dedicated processing hardware can be a single point of failure for a cell.

[0002] The virtualized radio access network can utilize one or more data centers with general computing resources to perform RAN processing for one or more cells. That is, instead of performing PHY and MAC layer processing locally on dedicated hardware, the virtualized radio access network can forward radio signals from the radio unit to the edge data center for processing, and similarly forward signals from the edge data center to the radio unit for wireless transmission. In a specific example, a cloud computing environment can be used to provide mobile edge computing (MEC), where certain functions of the mobile network can be provided as workloads on nodes in the cloud computing environment. In MEC, a centralized unit (CU) can be implemented in a backend node, one or more distributed units (DU) can be implemented in an intermediate node, and various radio units (RU) can be deployed at an edge server connected to an antenna. RU can communicate with CU via one or more DUs. In the example, DU can provide higher network layer functions for RAN, such as radio link control (RLC) or packet data convergence protocol (PDCP) layer functions. RU can facilitate access to CU for various downstream devices (such as user equipment (UE), Internet of Things (IoT) devices, etc.).

[0003] Because data centers utilize general-purpose computing resources, virtualized RAN can provide scalability and fault tolerance for base station processing. For example, a data center can allocate a variable number of computing resources (e.g., servers) to a radio unit to perform processing based on workload. In addition, virtualized RAN can implement multi-layer RAN processing at the data center, enabling the collection of multiple data feeds.

[0004] Tiered licensed deployments are systems deployed in a shared access band with two or more tiers of licensees. For example, the Citizens Broadband Radio Service (CBRS) band has a three-tier access and licensing framework for the 3550 to 3700 MHz (3.5 GHz) band. The three tiers include incumbent access, priority access, and general authorized access (GAA). GAA users shall not cause harmful interference to incumbent access users or priority access licensees, and shall accept interference from these users. AA users have no expectation of interference protection from other GAA users. Summary of the invention

[0005] A simplified summary of one or more aspects is given below to provide a basic understanding of these aspects. This summary is not an extensive overview of all contemplated aspects, and is intended to neither identify key elements or important elements of all aspects, nor to describe the scope of any or all aspects. Its sole purpose is to present some concepts of one or more aspects in a simplified form as a prelude to the detailed embodiments presented later.

[0006] In some aspects, the technology described herein relates to an apparatus for wireless communications, comprising: a memory storing computer-executable instructions; and a processor coupled to the memory and configured to execute the instructions to: check a local spectrum access database of known general authorized access (GAA) users to determine that a portion of the shared use spectrum is not occupied by known local users in a geographic area; receive a sample of a wireless signal, wherein the wireless signal includes a desired signal on at least the portion of the shared use spectrum; determine whether the wireless signal includes multiple independent signals; and in response to determining that the wireless signal includes multiple independent signals, identify an interfering signal.

[0007] In some aspects, the techniques described herein relate to an apparatus wherein the processor is further configured to mitigate interfering signals.

[0008] In some aspects, the techniques described herein relate to an apparatus wherein, to mitigate interfering signals, the processor is further configured to shift carrier frequencies within a shared use spectrum.

[0009] In some aspects, the techniques described herein relate to an apparatus wherein, to mitigate the interfering signal, the processor is further configured to add the interfering signal to a local spectrum access database.

[0010] In some aspects, the technology described herein relates to an apparatus wherein the processor is further configured to determine a difference between an expected decoding rate and an actual decoding rate, wherein the expected decoding rate is based on a measured signal quality, wherein in response to the difference exceeding a threshold, determine whether the wireless signal includes multiple independent signals.

[0011] In some aspects, the technology described herein relates to an apparatus wherein, to determine whether a wireless signal includes multiple independent signals, a processor is configured to apply a sample of the wireless signal to a machine learning-based source separation algorithm.

[0012] In some aspects, the technology described herein relates to an apparatus wherein, to determine whether a wireless signal includes multiple independent signals, a machine learning-based source separation algorithm is configured to indicate multiple copies of a desired signal with different delays.

[0013] In some aspects, the technology described herein relates to an apparatus wherein, to determine whether a wireless signal includes multiple independent signals, a machine learning-based source separation algorithm is configured to indicate at least one signal that is independent of a desired signal.

[0014] In some aspects, the technology described herein relates to an apparatus in which a processor is configured to: check a spectrum access system (SAS) database of licensed users, wherein the licensed users have a higher access level than GAA users; and select a portion of the shared use spectrum to avoid interference with the licensed users.

[0015] In some aspects, the technology described herein relates to a method comprising: checking a local spectrum access database of known general authorized access (GAA) users to determine that a portion of the shared use spectrum is not occupied by known local users in a geographic area; receiving a sample of a wireless signal, wherein the wireless signal includes a desired signal on at least the portion of the shared use spectrum; determining whether the wireless signal includes multiple independent signals; and in response to determining that the wireless signal includes multiple independent signals, identifying an interfering signal.

[0016] In some aspects, techniques described herein relate to a method and also include mitigating an interfering signal.

[0017] In some aspects, the techniques described herein relate to a method in which mitigating an interfering signal includes shifting a carrier frequency within a shared-use spectrum.

[0018] In some aspects, the techniques described herein relate to a method in which mitigating an interfering signal includes adding the interfering signal to a local spectrum access database.

[0019] In some aspects, the technology described herein relates to a method, further comprising: determining a difference between an expected decoding rate and an actual decoding rate, wherein the expected decoding rate is based on a measured signal quality, wherein in response to the difference exceeding a threshold, determining whether the wireless signal includes multiple independent signals.

[0020] In some aspects, the technology described herein relates to a method in which determining whether a wireless signal includes multiple independent signals includes applying a sample of the wireless signal to a machine learning based source separation algorithm.

[0021] In some aspects, the technology described herein relates to a method in which determining whether a wireless signal includes multiple independent signals includes an output of a machine learning-based source separation algorithm that indicates multiple copies of a desired signal with different delays.

[0022] In some aspects, the technology described herein relates to a method in which determining whether a wireless signal includes multiple independent signals includes an output of a machine learning-based source separation algorithm that indicates at least one signal that is independent of a desired signal.

[0023] In some aspects, the technology described herein relates to a method, further comprising: checking a spectrum access system SAS database of licensed users, wherein the licensed users have a higher access level than GAA users; and selecting a portion of the shared use spectrum to avoid interference with the licensed users.

[0024] In some aspects, the technology described herein relates to a non-transitory computer-readable medium storing computer-executable instructions that, when executed by a processor, cause the processor to: check a local spectrum access database of known general authorized access (GAA) users to determine that a portion of the shared use spectrum is not licensed for occupation by known local users in a geographic area; receive a sample of a wireless signal, the wireless signal including a desired signal on at least the portion of the shared use spectrum; determine whether the wireless signal includes multiple independent signals; and in response to determining that the wireless signal includes multiple independent signals, identify an interfering signal.

[0025] In some aspects, the techniques described herein relate to a non-transitory computer-readable medium further comprising instructions for mitigating an interfering signal.

[0026] To accomplish the foregoing and related ends, one or more aspects include features hereinafter fully described and particularly pointed out in the claims. The following description and the accompanying drawings set forth in detail certain illustrative features of one or more aspects. However, these features are indicative of only some of the various ways in which the principles of the various aspects may be employed, and this description is intended to include all such aspects and their equivalents. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 is a diagram of an example mobile network including a radio access network (RAN) connecting user equipment (UE) to a core network.

[0028] Figure 2 is a diagram of an example deployment scenario in which a first General Authorization Access (GAA) user experiences interference from a second GAA user.

[0029] Figure 3is a schematic diagram of an example of an apparatus for implementing a virtual distributed unit including an interference detection component.

[0030] Figure 4 is a flow chart of an example of a method for interference detection by a GAA user.

[0031] Figure 5 Shown include Figure 3 Additional optional components are shown to detail an example of device 500 . DETAILED DESCRIPTION

[0032] The specific embodiments described below in conjunction with the accompanying drawings are intended as descriptions of various configurations and are not intended to represent the only configurations in which the concepts described herein may be practiced. The specific embodiments include specific details for the purpose of providing a thorough understanding of the various concepts. However, it will be apparent to those skilled in the art that these concepts may be practiced without these specific details. In some cases, well-known components are shown in block diagram form to avoid obscuring such concepts.

[0033] The present disclosure describes various examples related to the operation of a general authorized access (GAA) system within a hierarchical licensing system. For example, in the United States, the Citizens Band Radio Service (CBRS) spectrum is available in the 3.5GHz (from 3550MHz to 3700MHz) band. The highest level is existing access, which includes federal users, fixed satellite service earth stations, and some traditional wireless broadband licensees. Existing access users are protected from harmful interference from priority access licensed users and general licensed access users. The second level is the priority access license (PAL), which has a license for a 10MHz channel within the CBRS spectrum. PAL must protect and accept interference from existing access users, but receive protection from general licensed access users. The third level is GAA. GAA users shall not cause harmful interference to existing access users or priority access licensees, and shall accept interference from these users. GAA users have no expectation of interference protection from other GAA users.

[0034] In order to provide protection for existing access users and PAL users, the spectrum access system (SAS) provides information about the use by existing access users and PAL users. Therefore, GAA users can access SAS to avoid interference to higher-level users and avoid interference signals from higher-level users. However, SAS does not provide any information about other GAA users. Because GAA users have no expectations about interference protection for other GAA users, the GAA system may experience interference from other GAA users. Therefore, it is expected that enterprises implementing the GAA system can try to identify and mitigate interference signals from other GAA users. In traditional licensed systems, licensees can be protected from external interference sources, so interference detection technology may not be necessary, or may be limited to interference detection between devices within the licensed system. In traditional unlicensed systems, technologies such as clear channel assessment (CCA) and listen before talk (LBT) can be used to share spectrum. However, for GAA users, it may not be guaranteed that interference sources will share spectrum based on such technologies.

[0035] In a software radio access network (also known as virtualized RAN or vRAN), various base station components can be implemented in software that executes on general-purpose computing resources. For example, in a cloud network implementation, vRAN components can execute as workloads on a server or data center. A key transformation of the radio access network (RAN) in 5G is the migration to an open RAN architecture, which enables 5G RAN to be virtualized and decoupled across multiple open interfaces. This approach promotes innovation by allowing multiple vendors to come up with unique solutions for different components at a faster pace. In addition, a new component called the Radio Intelligent Controller (RIC) is introduced in the open RAN architecture, which allows third parties to build new, vendor-agnostic monitoring and optimization use cases through O-RAN standardized interfaces.

[0036] In one aspect, the present disclosure provides a system for identifying and mitigating interference signals from other GAA users. The system may maintain and / or access a local spectrum access database of known GAA users to determine whether a portion of the shared spectrum is not occupied by known local users in a geographic area. Unlike the SAS for higher-level users, the local spectrum access database may be voluntary and may be incomplete or outdated. The system may also detect other GAA users that are not in the local spectrum access database. The decoupled O-RAN architecture may provide access to in-phase and quadrature (IQ) samples received at a network entity such as a base station. The network operator was previously unable to obtain such IQ samples, or the network operator had little control over the processing of such IQ samples. In one aspect, the system may use machine learning signal separation techniques applied to the received IQ samples to identify signals that actually interfere with the desired signal. For example, the desired signal may undergo multipath propagation and arrive at the receiver as a superposition of multiple copies of the transmitted signal. This multipath propagation may appear to be interference, but may not be considered to be an actual interference signal from outside the system. Interference signals from outside the system may be independent of the desired signal. The machine learning signal separation technique may output independent signals, and / or the received signal may include the possibility of multiple independent signals. Thus, the system may detect signals that are received simultaneously with the desired signal and interfere with the desired signal. In some implementations, the system may be configured to mitigate interference from such external interfering signals. For example, the system may select different parts of the shared spectrum to avoid interfering signals. In some implementations, the system may add interfering signals to a local spectrum access database to facilitate avoiding interfering signals.

[0037] Now turn to Figures 1 to 5 , examples are depicted with reference to one or more components and one or more methods that can perform the actions or operations described herein, where the components and / or actions / operations in dashed lines may be optional. Figure 4 The operations described in the description are presented in a particular order and / or performed by example components, but in some examples, the ordering of the actions and components performing the actions may vary depending on the implementation. In addition, in some examples, one or more of the actions, functions, and / or described components may be performed by a specially programmed processor, a processor executing specially programmed software or computer-readable media, or by any other combination of hardware components and software components capable of performing the described actions or functions.

[0038] Figure 11 is a diagram of an example mobile network 100 including a radio access network (RAN) that connects a user equipment (UE) 110 to a core network 160. The RAN may be implemented at least in part as a virtualized radio access network (vRAN) 102. The vRAN 102 may include radio units (RUs) 120 that transmit and receive wireless signals with the UE 110 over wireless links 112. Each RU 120 may provide a cell 122 having a coverage area that may overlap with other cells. Each RU 120 may include one or more antennas for transmitting and receiving radio frequency (RF) signals with the UE 110 within the cell 122.

[0039] The vRAN 102 may include virtual network components that may be implemented on general-purpose computing resources such as in a cloud network 104. The cloud network 104 may include an underlying wide area network (WAN) having computing resources such as servers or data centers that may be used to instantiate software network functions. For example, the vRAN 102 may include one or more virtual distributed units (vDUs) 130 that perform processing for the cell 122, for example, at a physical (PHY) layer, a media access control (MAC) layer, and a radio link control (RLC) layer. The vRAN 102 may include one or more virtual central units (vCUs) 140 that perform processing at higher layers of the wireless protocol stack. In an example architecture, the vCU 140 may be divided into a central unit control plane (CU-CP) and a central unit user plane (CU-UP). The CU-UP may include a packet data convergence protocol (PDCP) layer and a service data adaptation (SDAP) layer and a radio resource control (RRC) layer.

[0040] The division of functions or protocol layers between the vDU 130 and the vCU 140 may depend on the functional split architecture. For example, as shown, the functional split may be between the RLC layer and the PDCP layer, and may be referred to as option 2. Other options may include a functional split between the RRC layer and the PDCP layer (option 1), or a functional split between or within layers as shown at the vDU 130. Thus, the vRAN 102 may allow network equipment vendors to provide network operators with greater flexibility in where computing resources for the various protocol layers are deployed.

[0041] The vRAN 102 may include a RAN Intelligent Control (RIC) 150 that performs autonomous configuration and optimization of the vRAN 102. In some implementations, the RIC 150 may allocate resources of the cloud network 104 for instantiating vRAN components, such as vDUs 130 and vCUs 140. For example, the RIC 150 may provide a configuration file for each vDU 130 or vCU 140. The RIC 150 may allocate network resources such as frequency subbands for cells. In addition, the RIC may monitor the performance of the vRAN 102 and adapt the configuration. For example, the RIC 150 may receive monitoring information, such as performance metrics, from vRAN components, such as vDUs 130 and vCUs 140.

[0042] In one aspect, the RIC 150 may include a GAA control component 152 configured to control the deployment of the vRAN 102 as a GAA user on a shared access band subject to hierarchical licensing. The GAA control component 152 may communicate with an interference detection component 170, which may be located at the vDU 130. For example, the interference detection component 170 may run within or in parallel with the PHY layer processing. For example, as described in further detail below, the interference detection component 170 may receive IQ samples from the RU 120 and detect interference signals based on the IQ samples. The GAA control component 152 may also communicate with a local spectrum access database 180 and a SAS database 182. The SAS database 182 may provide information about existing access users and / or PAL users. Based on the GAA license, the GAA control component 152 may avoid interfering with such higher-level users.

[0043] The local spectrum access database 180 may store information about GAA users. In some implementations, the GAA control component 152 may host or control the local spectrum access database 180. Alternatively, the local spectrum access database 180 may be a publicly accessible database or a proprietary database provided by a third party, such as on a subscription basis. In some implementations, the GAA control component 152 includes a mitigation component 154 that is configured to mitigate interference caused by an interfering signal from another GAA user. Information about other GAA users may be stored in the local spectrum access database 180.

[0044] Participation in the local spectrum access database 180 may be voluntary. For example, an enterprise that is a GAA user may provide information about its system (e.g., vRAN 102) so that other GAA users can avoid interference. Similarly, an enterprise may want to obtain information about neighboring GAA users to avoid causing and receiving interference from neighboring GAA users. However, some GAA users may not want to provide information about network deployment to other GAA users. In some implementations, information about these GAA users may be added to the local spectrum access database 180 when interference from these GAA users is detected and becomes public knowledge.

[0045] Figure 2 200 is a diagram of an example deployment scenario in which a first GAA user experiences interference from a second GAA user. The first GAA user may deploy a RU 220 that provides a cell 222 for wireless communication with a UE 110. The RU 220 may communicate with a vDU 130 that performs PHY processing for the cell. The vDU 130 may be controlled by the RIC 150 of the first GAA user.

[0046] The second GAA user may deploy RU 240, which provides a cell 242 for communicating with UE 250. In some implementations, there may be no coordination between the first GAA user and the second GAA user. Downlink signals from RU 240 and / or uplink signals from UE 250 may interfere with reception at RU 220. In a licensed spectrum, this interference can be avoided because only one party controls the spectrum and the party can coordinate RUs to avoid cross-link interference. In an unlicensed spectrum, users may follow CCA or LBT procedures to avoid interference. However, in a hierarchical access system, GAA users may cause interference to each other.

[0047] The interference detection component 170 at the vDU can detect a signal that interferes with the desired signal. For example, the UE 110 can transmit the desired signal 210 to the RU 220. The desired signal 210 can propagate along multiple paths, such as a line of sight path 212, and a second path 214 reflected by one or more objects 216. From the perspective of the RU 220, the desired signal 210 arriving via multiple paths may appear to be an interference signal, but is actually a time-delayed version of the same signal. The actual interference signal 252 can be transmitted by the RU 240 or the UE 250. The interference signal 252 can be independent of the desired signal 210.

[0048] In one aspect, the interference detection component 170 can determine whether the wireless signal received at the RU 220 includes multiple independent signals. For example, the interference detection component 170 can apply the received IQ samples to a machine learning (ML) source separation algorithm. The ML source separation algorithm can utilize a deep neural network (DNN) model. For example, Grais et al. describe a DNN for single-channel source separation that can be used by the interference detection component 170 in "Deep Neural Networks for Single Channel Source Separatio" (arXiv: 1311.2746v1), where samples of audio signals are applied to the model to separate vocal and instrument sources. In order to separate the wireless signal source in the IQ sample, training data for the desired signal can be obtained by collecting samples from interference-free transmissions. The training data also includes transmissions interfered by controlled transmissions. The training data can also be supplemented with simulated transmissions. Training can be performed in an offline manner. However, when the bit error rate is statistically much higher than the bit error rate expected based on the signal quality, the training data set can be updated.

[0049] The ML source separation algorithm may output one or more independent signals. If the ML source separation algorithm outputs a single independent signal, the interference detection component 170 may determine that the received signal includes multiple copies of the desired signal 210 with different delays. If the ML source separation algorithm outputs multiple independent signals, the interference detection component 170 may determine that the received signal includes the interfering signal 252.

[0050] In some implementations, the interference detection component 170 can be triggered to detect the interference signal 252 based on performance characteristics. For example, the interference detection component 170 can monitor a decoding rate or an error rate (e.g., a block error rate (BLER)) and determine a difference between an expected decoding rate and an actual decoding rate, where the expected decoding rate is based on the measured signal quality. If the difference exceeds a threshold, the interference detection component 170 can determine whether the wireless signal includes multiple independent signals.

[0051] The interference detection component 170 can provide information about the interference signal 252 to the GAA control component 152. For example, the interference detection component 170 can identify the time when the interference signal 252 is detected or the receive beam when the interference signal 252 is detected.

[0052] In some implementations, the GAA control component 152 and / or the mitigation component 154 can attempt to mitigate the interference signal 252. For example, the GAA control component 152 and / or the mitigation component 154 can shift the carrier frequency for the RU 220 within the shared use spectrum, or otherwise allocate different frequency resources to avoid the interference signal 252. As another example, the GAA control component 152 and / or the mitigation component 154 can add the interference signal to the local spectrum access database 180. As a result, the GAA control component 152 and other GAA systems can avoid the interference signal 252.

[0053] Figure 3 3 is a schematic diagram of an example of an apparatus 300 (e.g., a computing device) for implementing a vDU 130 including an interference detection component 170. The apparatus 300 may be an example of a network entity. The apparatus 300 may reside in a data center, which may be an edge data center. The apparatus 300 may be connected to other servers in the data center or other data centers via a switch 340. For example, the apparatus 300 may be connected to a RU 120, a vCU 140, and a RIC 150. In some implementations, one or more of the RU 120, the vCU 140, or the RIC 150 may implement the interference detection component 170 or a component thereof.

[0054] In an example, the apparatus 300 may include a processor 302 and / or a memory 304 configured to execute or store instructions or other parameters related to providing an operating system 306, which may execute one or more applications or processes, such as, but not limited to, the interference detection component 170. For example, the processor 302 and the memory 304 may be separate components communicatively coupled via a bus (e.g., on a motherboard or other portion of a computing device, on an integrated circuit such as a system on a chip (SoC), etc.), components integrated with each other (e.g., the processor 302 may include the memory 304 as an onboard component), etc. The memory 304 may store instructions, parameters, data structures, etc., for use / execution by the processor 302 to perform the functions described herein.

[0055] In an example, the interference detection component 170 can include one or more of a configuration component 172 , a reception component 174 , a separation component 176 , or an identification component 178 .

[0056] The configuration component 172 may be configured to check a local spectrum access database 180 of known GAA users to determine that a portion of the shared spectrum is not occupied by known local users in a geographic area. For example, the interference detection component 170 may provide the geographical coordinates of the RU 120 to the GAA control component 152 for comparison with the local spectrum access database 180. For example, the GAA control component 152 may query the local spectrum access database 180 using the geographical coordinates of known GAA users within an interference distance (e.g., 1km for a macro cell) and compare the portion of the shared spectrum with the portion of the spectrum used by the known GAA user. In some implementations, the configuration component 172 and / or the GAA control component 152 may also check a SAS database 182 of licensed users with access levels higher than the GAA user. In some implementations, the configuration component 172 and / or the GAA control component 152 may select the portion of the shared spectrum to avoid interference with licensed users and / or other GAA users.

[0057] The receiving component 174 may be configured to receive a sample of a wireless signal including a desired signal at least on a portion of the shared use spectrum. For example, the receiving component 174 may receive an IQ sample from the RU 120. In some implementations, the receiving component 174 may include a network interface controller (NIC) configured to receive IP packets via the switch 340. The receiving component 174 may be configured to implement an O-RAN interface for receiving IQ samples within an IP packet. The sample may include at least the desired signal 210. For example, when an uplink transmission from the UE 110 is scheduled, the sample may be collected. The sample may also include an interference signal 252.

[0058] The separation component 176 can be configured to determine whether the wireless signal includes multiple independent signals. For example, the separation component 176 can be configured to apply the samples to an ML source separation algorithm. The separation component 176 can output the number of independent signals or the actual independent signals.

[0059] The identification component 178 can be configured to: in response to determining that the wireless signal includes multiple independent signals, identify the interference signal. For example, the identification component 178 can identify the interference signal 252 to the GAA control component 152 and / or the local spectrum access database 180. The identification component 178 can identify the interference signal 252 by detecting the frequency range or other signal properties of the interference signal 252.

[0060] In some implementations, the apparatus 300 may include a GAA control component 152. For example, the GAA control component 152 may be implemented at the vDU 130, or the vDU 130 and the RIC may be implemented on the same data center. The GAA component 152 may be configured to communicate with the local spectrum access database 180 and / or the SAS database 182. The GAA component 152 may include a mitigation component 154 configured to mitigate the interference signal 252. For example, the GAA component 152 may shift the carrier frequency within the shared use spectrum, or add the interference signal 252 to the local spectrum access database 180.

[0061] Figure 4 4 is a flow chart of an example of a method 400 for interference detection by a GAA user. For example, the method 400 may be performed by the apparatus 300 and / or one or more components thereof to detect the interference signal 252. Some blocks of the method 400 may optionally be performed by other network components in communication with the apparatus 300.

[0062] At block 410, method 400 optionally includes checking a SAS database of a permitted user with a higher access level than the GAA user. In an example, configuration component 172 (e.g., in conjunction with processor 302, memory 304, and operating system 306) can check a SAS database 182 of a permitted user with a higher access level than the GAA user. For example, configuration component 172 can access SAS database 182 directly or via GAA control component 152.

[0063] At block 420, method 400 optionally includes selecting a portion of the shared use spectrum to avoid interference with licensed users. In an example, configuration component 172 (e.g., in conjunction with processor 302, memory 304, and operating system 306) may select a portion of the shared use spectrum to avoid interference with licensed users. For example, configuration component 172 may receive an indication of a portion of the shared use spectrum from GAA control component 152, or autonomously select a portion of the shared use spectrum that is not used by licensed users.

[0064] At block 430, method 400 includes checking a local spectrum access database of known GAA users to determine that the portion of the shared use spectrum is not occupied by known local users in the geographic area. In an example, configuration component 172 (e.g., in conjunction with processor 302, memory 304, and operating system 306) can check a local spectrum access database 180 of known GAA users to determine that the portion of the shared use spectrum is not occupied by known local users in the geographic area. For example, configuration component 172 can access local spectrum access database 180 directly or via GAA control component 152.

[0065] At block 440, method 400 includes receiving samples of wireless signals including a desired signal at least on a portion of the shared use spectrum. In an example, receiving component 174 (e.g., in conjunction with processor 302, memory 304, and operating system 306) can receive samples of wireless signals including a desired signal at least on a portion of the shared use spectrum. For example, receiving component 174 can receive samples from RU 120.

[0066] At block 450, method 400 may optionally include determining a difference between an expected decoding rate and an actual decoding rate, the expected decoding rate being based on the measured signal quality. In an example, receiving component 174 (e.g., in conjunction with processor 302, memory 304, and operating system 306) may determine a difference between an expected decoding rate and an actual decoding rate, the expected decoding rate being based on the measured signal quality.

[0067] At block 460, method 400 includes determining whether the wireless signal includes multiple independent signals. In an example, separation component 176 (e.g., in conjunction with processor 302, memory 304, and operating system 306) may determine whether the wireless signal includes multiple independent signals. In some implementations, at subblock 462, block 460 may include applying a sample of the wireless signal to a machine learning-based source separation algorithm. The output of the machine learning-based source separation algorithm may indicate multiple copies of the desired signal with different delays, which are not considered multiple independent signals. Alternatively, the output of the machine learning-based source separation algorithm may indicate at least one signal independent of the desired signal.

[0068] At block 470, method 400 includes, in response to determining that the wireless signal includes a plurality of independent signals, identifying an interfering signal. In an example, identification component 178 (e.g., in conjunction with processor 302, memory 304, and operating system 306) may identify interfering signal 252 in response to determining that the wireless signal includes a plurality of independent signals.

[0069] At block 480, the method 400 may optionally include mitigating the interference signal. In an example, the mitigation component 154 (e.g., in conjunction with the processor 302, the memory 304, and the operating system 306) may mitigate the interference signal. For example, in some implementations, at sub-block 482, block 480 may include shifting the carrier frequency within the shared use spectrum. As another example, in some implementations, at sub-block 484, block 480 may include adding the interference signal 252 to the local spectrum access database 180.

[0070] Figure 5 Shown include Figure 3An example of device 500 with additional optional component details shown. In one aspect, device 500 may include a processor 502, which may be similar to processor 302, for performing processing functions associated with one or more of the components and functions described herein. Processor 502 may include a single or multiple processors or a multi-core processor. In addition, processor 502 may be implemented as an integrated processing system and / or a distributed processing system.

[0071] The device 500 may also include a memory 504, which may be similar to the memory 304, such as for storing a local version of an operating system (or its components), and / or applications executed by the processor 502, such as the interference detection component 170, the GAA control component 154, etc. The memory 504 may include the type of memory that can be used by a computer, such as random access memory (RAM), read-only memory (ROM), tape, magnetic disk, optical disk, volatile memory, non-volatile memory, and any combination thereof.

[0072] Additionally, device 500 may include a communication component 506 that provides for establishing and maintaining communications with one or more other devices, parties, entities, etc. utilizing hardware, software, and services as described herein. Communication component 506 may carry communications between components on device 500 and between device 500 and external devices, such as devices located across a communication network, and / or devices serially or locally connected to device 500. For example, communication component 506 may include one or more buses, and may also include a transmit chain component and a receive chain component, respectively associated with a wireless or wired transmitter and a receiving machine operable to interface with external devices.

[0073] Additionally, the device 500 may include a data store 508, which may be any suitable combination of hardware and / or software that provides mass storage of information, databases, and programs employed in conjunction with aspects described herein. For example, the data store 508 may be or may include a data repository, application programs, related parameters, etc. for an operating system (or components thereof) that are not currently executed by the processor 502. Additionally, the data store 508 may be a data repository for the interference detection component 170, the GAA control component 152, and / or one or more other components of the device 500.

[0074] Device 500 may optionally include user interface component 510, which is operable to receive input from a user of device 500, and is also operable to generate output for presentation to the user. User interface component 510 may include one or more input devices, including but not limited to a keyboard, a numeric keypad, a mouse, a touch-sensitive display, a navigation key, a function key, a microphone, a voice recognition component, a gesture recognition component, a depth sensor, a gaze tracking sensor, a switch / button, any other mechanism capable of receiving input from a user, or any combination thereof. In addition, user interface component 510 may include one or more output devices, including but not limited to a display, a speaker, a tactile feedback mechanism, a printer, any other mechanism capable of presenting output to a user, or any combination thereof.

[0075] The device 500 can additionally include an interference detection component 170 for detecting interference signals 252 and / or a GAA control component 152 for avoiding interference, as described herein.

[0076] As an example, any part of an element or any combination of elements can be implemented with a "processing system" including one or more processors. Examples of processors include microprocessors, microcontrollers, digital signal processors (DSPs), field programmable gate arrays (FPGAs), programmable logic devices (PLDs), state machines, gated logic, discrete hardware circuits, and other suitable hardware configured to perform various functions described throughout the present disclosure. One or more processors in a processing system can execute software. Software should be broadly interpreted as meaning instructions, instruction sets, codes, code segments, program codes, programs, subroutines, software modules, applications, software applications, software packages, routines, subroutines, objects, executable files, threads of execution, processes, functions, etc., whether it is referred to as software, firmware, middleware, microcode, hardware description language, or other.

[0077] Therefore, in one or more aspects, one or more of the functions described can be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the function can be stored on a computer-readable medium or encoded as one or more instructions or codes on a computer-readable medium. Computer-readable media include computer storage media. Storage media can be any available medium that can be accessed by a computer. As an example and not limitation, such a computer-readable medium may include RAM, ROM, EEPROM, CD-ROM or other optical disk storage, disk storage or other magnetic storage device, or any other medium, which can be used to carry or store the desired program code in the form of an instruction or data structure and can be accessed by a computer. As used herein, disks and optical disks include compact disks (CDs), laser disks, optical disks, digital versatile disks (DVDs), and floppy disks, wherein disks usually reproduce data magnetically, and optical disks reproduce data optically with lasers. The above combination should also be included in the scope of computer-readable media.

[0078] The foregoing description is provided to enable those skilled in the art to practice the various aspects described herein. It will be apparent to those skilled in the art that various modifications to these aspects, and the general principles defined herein can be applied to other aspects. Therefore, the claims are not intended to be limited to the aspects shown herein, but to the full range consistent with the claim language. Unless specifically stated so, references to singular elements are not intended to mean "one and only one", but "one or more". Unless otherwise specifically stated, the term "some" refers to one or more. In addition, the term "or" is intended to mean an inclusive "or" rather than an exclusive "or". That is, unless otherwise stated, or clearly stated in the context, the phrase "X adopts A or B" is intended to mean any natural inclusive replacement. That is, the phrase "X adopts A or B" is satisfied by any of the following examples: X adopts A; X adopts B; or X adopts both A and B. In addition, the articles "a" and "an" as used in the present application and the appended claims should generally be interpreted as meaning "one or more", unless otherwise stated or clearly specified as a singular form from the context. Furthermore, nothing disclosed herein is intended to be dedicated to the public regardless of whether such disclosure is explicitly recited in the claims.A claim element is not to be construed as a means-plus-function unless the element is explicitly recited using the phrase "means for..."

Claims

1. A device for wireless communication, comprising: a memory storing computer executable instructions; as well as a processor coupled to the memory and configured to execute the instructions to: Checking the local spectrum access database of known General Authorized Access (GAA) users to determine portions of the shared use spectrum that are not occupied by known local users in the geographic area; receiving samples of wireless signals including at least a desired signal on the portion of the shared use spectrum; determining whether the wireless signal includes a plurality of independent signals; and In response to determining that the wireless signal includes a plurality of independent signals, an interfering signal is identified. 2 . The apparatus of claim 1 , wherein the processor is further configured to mitigate the interfering signal. 3 . The apparatus of claim 2 , wherein to mitigate the interference signal, the processor is further configured to shift a carrier frequency within the shared spectrum.

4. The apparatus of claim 2, wherein to mitigate the interference signal, the processor is further configured to add the interference signal to the local spectrum access database.

5. The apparatus of claim 1 , wherein the processor is further configured to determine a difference between an expected decoding rate and an actual decoding rate, wherein the expected decoding rate is based on measured signal quality, and wherein in response to the difference exceeding a threshold, determining whether the wireless signal comprises multiple independent signals.

6. The apparatus of claim 1, wherein to determine whether the wireless signal comprises a plurality of independent signals, the processor is configured to apply the samples of the wireless signal to a machine learning-based source separation algorithm.

7. The apparatus of claim 6, wherein to determine whether the wireless signal comprises multiple independent signals, the machine learning based source separation algorithm is configured to indicate multiple copies of the desired signal with different delays.

8. The apparatus of claim 6, wherein to determine whether the wireless signal includes multiple independent signals, the machine learning-based source separation algorithm is configured to indicate at least one signal that is independent of the desired signal.

9. The apparatus of claim 1, wherein the processor is configured to: checking a spectrum access system SAS database of a licensed user, wherein the licensed user has a higher access level than the GAA user; and The portion of the shared use spectrum is selected to avoid interference with the licensed users.

10. A method comprising: Check the local spectrum access database of known GAA users to determine which parts of the shared use spectrum are not occupied by known local users in the geographic area; receiving samples of wireless signals including at least a desired signal on the portion of the shared use spectrum; determining whether the wireless signal includes a plurality of independent signals; and In response to determining that the wireless signal includes a plurality of independent signals, an interfering signal is identified.

11. The method according to claim 10, further comprising: The interfering signal is mitigated.

12. The method of claim 11, wherein mitigating the interfering signal comprises: The carrier frequency within the shared spectrum is shifted, or the interference signal is added to the local spectrum access database.

13. The method according to claim 10, further comprising: A difference between an expected decoding rate and an actual decoding rate is determined, wherein the expected decoding rate is based on a measured signal quality, wherein in response to the difference exceeding a threshold, a determination is made whether the wireless signal includes multiple independent signals.

14. The method of claim 10, wherein determining whether the wireless signal includes a plurality of independent signals comprises: The samples of the wireless signal are applied to a machine learning based source separation algorithm.

15. A computer readable medium storing computer executable instructions which, when executed by a processor, cause the processor to perform the method of claim 10.

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